Evidence map›Paper›PMID 36687586›Full record

ReviewFrontiers in microbiology2022

Research progress of electrochemical oxidation and self-action of electric field for medical wastewater treatment.

Jun Tang, Heng Zheng, Jinzhong Cai, Jiang Liu, Yangyang Wang, Jun Deng

Abstract readReview
In one paragraph

Review in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jun TangDepartment of Neurothoracic Surgery, The Third People's Hospital of Hubei Province Yangluo Campus, Jianghan University, Wuhan, China.
Heng ZhengDepartment of Neurothoracic Surgery, The Third People's Hospital of Hubei Province Yangluo Campus, Jianghan University, Wuhan, China.
Jinzhong CaiDepartment of Interventional Radiology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China.
Jiang LiuCollege of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, China.
Yangyang WangDepartment of Neurothoracic Surgery, The Third People's Hospital of Hubei Province Yangluo Campus, Jianghan University, Wuhan, China.
Jun DengCollege of Chemistry, Chemical Engineering and Life Sciences, Wuhan University of Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A large number of pathogenic microorganisms exist in medical wastewater, which could invade the human body through the water and cause harm to human health. With the global pandemic coronavirus (COVID-19), public health safety become particularly important, and medical wastewater treatment is an important part of it. In particular, electrochemical disinfection technology has been widely studied in medical wastewater treatment due to its greenness, high efficiency, convenient operation, and other advantages. In this paper, the development status of electrochemical disinfection technology in the treatment of medical wastewater is reviewed, and an electrochemical three-stage disinfection system is proposed for the treatment of medical wastewater. Moreover, prospects for the electrochemical treatment of medical wastewater will be presented. It is hoped that this review could provide insight and guidance for the research and application of electrochemical disinfection technology to treat medical wastewater.GRAPHICAL ABSTRACT.

Indexed as

disinfectionelectrochemical oxidationelectrolytic chlorinationmedical wastewaterself-action of electric field

Identifiers

PMID36687586
PMCPMC9853389

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.